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The Privacy Issue of Counterfactual Explanations: Explanation Linkage Attacks

https://doi.org/10.1145/3608482
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Outgoing claims: the papers this work cites, builds on, or argues with.

inferredcites →https://doi.org/10.1007/0-387-23483-7openalex-snapshot
Encyclopedia of Cryptography and Security
inferredcites →https://doi.org/10.1007/978-3-030-58112-1_31openalex-snapshot
Multi-Objective Counterfactual Explanations
inferredcites →https://doi.org/10.1007/978-3-642-10233-2_7openalex-snapshot
kACTUS 2: Privacy Preserving in Classification Tasks Using k-Anonymity
inferredcites →https://doi.org/10.1007/bf01096763openalex-snapshot
Greedy Randomized Adaptive Search Procedures
inferredcites →https://doi.org/10.1007/s10618-022-00831-6openalex-snapshot
Counterfactual explanations and how to find them: literature review and benchmarking
inferredcites →https://doi.org/10.1007/s10618-023-00930-yopenalex-snapshot
NICE: an algorithm for nearest instance counterfactual explanations
inferredcites →https://doi.org/10.1016/j.cose.2021.102488openalex-snapshot
k -Anonymity in practice: How generalisation and suppression affect machine learning classifiers
inferredcites →https://doi.org/10.1016/j.eswa.2012.02.179openalex-snapshot
Utility-preserving transaction data anonymization with low information loss
inferredcites →https://doi.org/10.1016/j.eswa.2023.121954openalex-snapshot
Generating collective counterfactual explanations in score-based classification via mathematical optimization
inferredcites →https://doi.org/10.1088/1757-899x/225/1/012279openalex-snapshot
An Extensive Study on Data Anonymization Algorithms Based on K-Anonymity
inferredcites →https://doi.org/10.1093/oso/9780192847263.001.0001openalex-snapshot
Data Science Ethics
inferredcites →https://doi.org/10.1109/cogmi56440.2022.00012openalex-snapshot
Evaluating the Privacy Exposure of Interpretable Global Explainers
inferredcites →https://doi.org/10.1109/icde.2005.42openalex-snapshot
Data Privacy through Optimal k-Anonymization
inferredcites →https://doi.org/10.1109/icde.2006.101openalex-snapshot
Mondrian Multidimensional K-Anonymity
inferredcites →https://doi.org/10.1109/icde.2007.367856openalex-snapshot
t-Closeness: Privacy Beyond k-Anonymity and l-Diversity
inferredcites →https://doi.org/10.1109/icde11332.2006openalex-snapshot
2007 IEEE 23rd International Conference on Data Engineering
inferredcites →https://doi.org/10.1109/icebe.2016.024openalex-snapshot
K-Anonymity through the Enhanced Clustering Method
inferredcites →https://doi.org/10.1109/ssci47803.2020openalex-snapshot
2020 IEEE Symposium Series on Computational Intelligence (SSCI)
inferredcites →https://doi.org/10.1109/tkde.2008.129openalex-snapshot
k-Anonymization with Minimal Loss of Information
inferredcites →https://doi.org/10.1109/tvcg.2019.2934619openalex-snapshot
The What-If Tool: Interactive Probing of Machine Learning Models
inferredcites →https://doi.org/10.1142/s0218488502001648openalex-snapshot
k-ANONYMITY: A MODEL FOR PROTECTING PRIVACY
inferredcites →https://doi.org/10.1142/s021848850200165xopenalex-snapshot
ACHIEVING k-ANONYMITY PRIVACY PROTECTION USING GENERALIZATION AND SUPPRESSION
inferredcites →https://doi.org/10.1145/1055558.1055591openalex-snapshot
On the complexity of optimal K-anonymity
inferredcites →https://doi.org/10.1145/1066157.1066164openalex-snapshot
Incognito
inferredcites →https://doi.org/10.1145/1150402.1150504openalex-snapshot
Utility-based anonymization using local recoding
inferredcites →https://doi.org/10.1145/1217299.1217302openalex-snapshot
L -diversity
inferredcites →https://doi.org/10.1145/1233321.1233324openalex-snapshot
Utility-based anonymization for privacy preservation with less information loss
inferredcites →https://doi.org/10.1145/1538909.1538911openalex-snapshot
A framework for efficient data anonymization under privacy and accuracy constraints
inferredcites →https://doi.org/10.1145/2810103.2813677openalex-snapshot
Model Inversion Attacks that Exploit Confidence Information and Basic Countermeasures
inferredcites →https://doi.org/10.1145/3375627.3375850openalex-snapshot
FACE
inferredcites →https://doi.org/10.1145/3436755openalex-snapshot
When Machine Learning Meets Privacy
inferredcites →https://doi.org/10.1145/3511299openalex-snapshot
Interpretable Machine Learning
inferredcites →https://doi.org/10.1145/3531146.3533235openalex-snapshot
Model Explanations with Differential Privacy
inferredcites →https://doi.org/10.1145/3546872openalex-snapshot
Trustworthy AI: A Computational Perspective
inferredcites →https://doi.org/10.1145/3624010openalex-snapshot
A Survey of Privacy Attacks in Machine Learning
inferredcites →https://doi.org/10.1145/775047.775089openalex-snapshot
Transforming data to satisfy privacy constraints
inferredcites →https://doi.org/10.1184/r1/6625769openalex-snapshot
Simple Demographics Often Identify People Uniquely
inferredcites →https://doi.org/10.1186/s40537-022-00579-2openalex-snapshot
The non-linear nature of the cost of comprehensibility
inferredcites →https://doi.org/10.1197/jamia.m2716openalex-snapshot
Protecting Privacy Using k-Anonymity
inferredcites →https://doi.org/10.1197/jamia.m3144openalex-snapshot
A Globally Optimal k-Anonymity Method for the De-Identification of Health Data
inferredcites →https://doi.org/10.2139/ssrn.3063289openalex-snapshot
Counterfactual Explanations Without Opening the Black Box: Automated Decisions and the GDPR
inferredcites →https://doi.org/10.2307/2983043openalex-snapshot
The Case for Samples of Anonymized Records from the 1991 Census
inferredcites →https://doi.org/10.24963/ijcai.2019/388openalex-snapshot
The Dangers of Post-hoc Interpretability: Unjustified Counterfactual Explanations
inferredcites →https://doi.org/10.25300/misq/2014/38.1.04openalex-snapshot
Explaining Data-Driven Document Classifications1
inferredcites →https://doi.org/10.3390/app11167274openalex-snapshot
A Framework and Benchmarking Study for Counterfactual Generating Methods on Tabular Data
inferredcites →https://doi.org/10.48550/arxiv.2009.01884openalex-snapshot
Model extraction from counterfactual explanations
inferredcites →https://doi.org/10.48550/arxiv.2206.14004openalex-snapshot
On the amplification of security and privacy risks by post-hoc explanations in machine learning models
inferredcites →https://doi.org/10.48550/arxiv.2211.05427openalex-snapshot
On the Privacy Risks of Algorithmic Recourse
inferredcites →https://doi.org/10.5120/1113-1457openalex-snapshot
Anonymity: An Assessment and Perspective in Privacy Preserving Data Mining
inferredcites →https://doi.org/10.5555/2870614.2870620openalex-snapshot
A Systematic Comparison and Evaluation of k-Anonymization Algorithms for Practitioners

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

inferred← citeshttps://doi.org/10.1007/978-981-95-7081-2_4openalex-snapshot
Watermarking Counterfactual Explanations
inferred← citeshttps://doi.org/10.1007/s10618-024-01017-yopenalex-snapshot
MCCE: Monte Carlo sampling of valid and realistic counterfactual explanations for tabular data
inferred← citeshttps://doi.org/10.1007/s11432-024-4123-4openalex-snapshot
Privacy-preserving explainable AI: a survey
inferred← citeshttps://doi.org/10.1007/s44196-024-00508-6openalex-snapshot
Introducing User Feedback-Based Counterfactual Explanations (UFCE)
inferred← citeshttps://doi.org/10.1016/j.cose.2025.104473openalex-snapshot
Privacy preservation techniques through data lifecycle: A comprehensive literature survey
inferred← citeshttps://doi.org/10.1016/j.jiixd.2026.05.005openalex-snapshot
Navigating the privacy-explainability-utility trilemma: A survey of differential privacy and explainable AI
inferred← citeshttps://doi.org/10.1016/j.knosys.2025.115235openalex-snapshot
Towards integration of privacy enhancing technologies in explainable artificial intelligence
inferred← citeshttps://doi.org/10.1016/j.segan.2026.102500openalex-snapshot
Explainability-guided counterfactual cyberattacks through surrogate modeling in power systems
inferred← citeshttps://doi.org/10.1109/access.2024.3441037openalex-snapshot
Counterfactual Explanations With Multiple Properties in Credit Scoring
inferred← citeshttps://doi.org/10.1109/aiot66900.2025.00046openalex-snapshot
Privacy-Preserving Explainable AIoT Application via SHAP Entropy Regularization
inferred← citeshttps://doi.org/10.1109/icct-europe63283.2025.11157698openalex-snapshot
Evaluating the Effectiveness of Differential Privacy Against Profiling
inferred← citeshttps://doi.org/10.1109/icsseecc69062.2026.11722174openalex-snapshot
Bias Breaker: A Privacy-Preserving and Generative Framework for Fairness Evaluation
inferred← citeshttps://doi.org/10.1109/isit63088.2025.11195552openalex-snapshot
Private Counterfactual Retrieval with Immutable Features
inferred← citeshttps://doi.org/10.1109/jsait.2026.3695917openalex-snapshot
Foundations of Efficient Model Reconstruction Using Counterfactual Explanations
inferred← citeshttps://doi.org/10.1109/ojcoms.2024.3386872openalex-snapshot
Explainable AI for 6G Use Cases: Technical Aspects and Research Challenges
inferred← citeshttps://doi.org/10.1109/pst65910.2025.11268815openalex-snapshot
Privacy Preservation with Noise in Explainable AI
inferred← citeshttps://doi.org/10.1109/tnnls.2023.3270027openalex-snapshot
Toward Explainable Affective Computing: A Review
inferred← citeshttps://doi.org/10.1145/3757887.3763008openalex-snapshot
Realizing the Promises of Algorithmic Recourse through Reliability, Accessibility, and Fairness Principles
inferred← citeshttps://doi.org/10.1145/3805689.3812361openalex-snapshot
Quantifying the Privacy of Counterfactuals by Leveraging Membership Inference Attacks Against Synthetic Data
inferred← citeshttps://doi.org/10.2139/ssrn.7519285openalex-snapshot
Balancing Utility and Privacy in NLP Counterfactual Explanations:An 𝜖token-Optimised DP-MLM-Inspired Framework
inferred← citeshttps://doi.org/10.2139/ssrn.7571295openalex-snapshot
PrivDiCE: Privacy-Preserving Actionable Counterfactual Explanations with Differential Privacy and Homomorphic Encryption
inferred← citeshttps://doi.org/10.23919/fusion59988.2024.10706413openalex-snapshot
Secure Counterfactual Explanations in a Two-party Setting
inferred← citeshttps://doi.org/10.3389/frai.2024.1236947openalex-snapshot
The privacy-explainability trade-off: unraveling the impacts of differential privacy and federated learning on attribution methods
inferred← citeshttps://doi.org/10.3390/electronics15061292openalex-snapshot
Explainable and Federated Recommender Systems: A Survey and Conceptual Framework for Trustworthy Personalization
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